Wavelet Transform Based Neural Network Model To Detect and Characterise ECG and EEG Signals Simultaneously

被引：0
|
作者：
Vedavathi, B. S. [1 ]
Biradar, Shilpa [2 ]
Hiremath, S. G. [1 ]
Thippeswamy, G. [3 ]
机构：
[1] East West Inst Technol, Dept ECE, Bangalore, Karnataka, India
[2] Dr Ambedkar Inst Technol, Dept ISE, Bangalore, Karnataka, India
[3] BMS Inst Technol, CSE, Bangalore, Karnataka, India
来源：
2015 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) | 2015年
关键词：
Neural Network; Electrocardiogram(ECG); Electroencephalogram (EEG); Dyadic wavelet transforms (DyWT); Daubechies Wavelet transforms(DWT); Back Propagation Neural Network; CLASSIFICATION; SEIZURE;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This research work focuses on to the development of neural network based detection and characterization of electrocardiogram (ECG) and electroencephalogram (EEG) signal. ECG and EEG signals have prime importance for patients under critical care. These signals have to be continuously monitored and processed as they are inter dependent. In this research Dyadic wavelet transform (DyWT) is used to process ECG data and Daubechies wavelet transform (DWT) is used to process EEG data. Emerging back propagation NN algorithm and Hopfield algorithm is used to detect and characterize both ECG and EEG signals. The different ECG and EEG data's have been collected and simultaneously processed and recognized.
引用
收藏
页码：743 / 748
页数：6
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